Abstract: Systems and methods for image processing may perform one or more operations including, but not limited to: receiving raw image data from at least one imaging device; computing at least one image depth distance from the raw image data; computing one or more image validity flags from the raw image data; generating at least one data validity mask from the one or more image validity flags; determining a background imagery estimation from at least one image depth distance; generating at least one foreground mask from the background imagery estimation and the at least one image depth distance; generating at least one region-of-interest mask from the data validity mask and the foreground mask; and generating filtered raw image data from the raw image data and at least one region of interest mask.

Abstract: An image processing system comprises an image processor having image processing circuitry and an associated memory. The image processor is configured to implement a foreground processing module utilizing the image processing circuitry and the memory. The foreground processing module is configured to obtain one or more images, to estimate a foreground region of interest from the one or more images, to determine a plurality of segments of the foreground region of interest, to calculate amplitude statistics for respective ones of the plurality of segments, to classify respective segments as being respective portions of static foreground objects or as being respective portions of dynamic foreground objects based at least in part on the calculated amplitude statistics and one or more defined patterns for known static and dynamic objects, and to remove one or more segments classified as static foreground objects from the foreground region of interest.

Abstract: A method for estimating error rates in low-density parity check codes includes calibrating an encoder according to specific channel parameters and according to dominant error events in the low-density parity-check code. Dominant codewords are classified based on characteristics of each codeword that are likely to produce similar error rates at similar noise levels; codeword classes that produce the highest error rate are then tested. Error boundary distance is estimated using multiple binary searches on segments. Segments are defined based on codeword, trapping set and biasing noise components of the channel. To improve calculation speed the most significant subclasses of codewords, trapping sets and noise signals are used.

Abstract: In one embodiment, an image processor comprises image processing circuitry and an associated memory. The image processor is configured to implement a gesture recognition system utilizing the image processing circuitry and the memory. The gesture recognition system implemented by the image processor comprises a dynamic gesture recognition module. The dynamic gesture recognition module is configured to establish a dynamic gesture recognition interval comprising a plurality of image frames, to extract one or more first features from the dynamic gesture recognition interval, to adjust the dynamic gesture recognition interval, to extract one or more second features from the adjusted dynamic gesture recognition interval, and to recognize a dynamic gesture based at least in part on at least a subset of the extracted first and second features.

Abstract: Systems and methods for image processing may perform one or more operations including, but not limited to: receiving raw image data from at least one imaging device; computing at least one image depth distance from the raw image data; computing one or more image validity flags from the raw image data; generating at least one data validity mask from the one or more image validity flags; determining a background imagery estimation from at least one image depth distance; generating at least one foreground mask from the background imagery estimation and the at least one image depth distance; generating at least one region-of-interest mask from the data validity mask and the foreground mask; and generating filtered raw image data from the raw image data and at least one region of interest mask.

Abstract: An image processing system comprises an image processor having image processing circuitry and an associated memory. The image processor is configured to implement an object tracking module. The object tracking module is configured to obtain one or more images, to extract contours of at least two objects in at least one of the images, to select respective subsets of points of the contours for the at least two objects based at least in part on curvatures of the respective contours, to calculate features of the subsets of points of the contours for the at least two objects, to detect intersection of the at least two objects in a given image, and to track the at least two objects in the given image based at least in part on the calculated features responsive to detecting intersection of the at least two objects in the given image.

Abstract: An image processing system comprises an image processor having image processing circuitry and an associated memory. The image processor is configured to implement a face recognition system utilizing the image processing circuitry and the memory, the face recognition system comprising a face recognition module. The face recognition module is configured to identify a region of interest in each of two or more images, to extract a three-dimensional representation of a head from each of the identified regions of interest, to transform the three-dimensional representations of the head into respective two-dimensional grids, to apply temporal smoothing to the two-dimensional grids to obtain a smoothed two-dimensional grid, and to recognize a face based on a comparison of the smoothed two-dimensional grid and one or more face patterns.

Abstract: An image processing system comprises an image processor having image processing circuitry and an associated memory. The image processor is configured to implement a foreground processing module utilizing the image processing circuitry and the memory. The foreground processing module is configured to obtain one or more images, to estimate a foreground region of interest from the one or more images, to determine a plurality of segments of the foreground region of interest, to calculate amplitude statistics for respective ones of the plurality of segments, to classify respective segments as being respective portions of static foreground objects or as being respective portions of dynamic foreground objects based at least in part on the calculated amplitude statistics and one or more defined patterns for known static and dynamic objects, and to remove one or more segments classified as static foreground objects from the foreground region of interest.

Abstract: The disclosure is directed to a system and method of image processing. According to various embodiments of the disclosure, a storage module in communication with a plurality of memory banks stores a plurality of pixels of an image in the memory banks and interleaves the memory banks to enable a plurality of image scanners to access the plurality of pixels. A scanning module scans a selection of pixels in at least four directions relative to a first pixel of the plurality of pixels utilizing the plurality of image scanners. A singular points detection module in communication with the scanning module acquires a depth of each pixel of the selection of scanned pixels and determines a singularity value of the first pixel by comparing the depth of the first pixel with the depth of each pixel of the selection of pixels.

Abstract: A method for estimating error rates in low-density parity check codes includes calibrating an encoder according to specific channel parameters and according to dominant error events in the low-density parity-check code. Dominant codewords are classified based on characteristics of each codeword that are likely to produce similar error rates at similar noise levels; codeword classes that produce the highest error rate are then tested. Error boundary distance is estimated using multiple binary searches on segments. Segments are defined based on codeword, trapping set and biasing noise components of the channel. To improve calculation speed the most significant subclasses of codewords, trapping sets and noise signals are used.